Summary measures, or indicators, of environmental conditions and
public health outcomes are the foundation of environmental health
tracking. To advance indicator development and use, the Johns Hopkins Center for Excellence in Environmental Public Health Tracking (JHU Tracking Center) evaluated three pilot indicator pairs: 1) air toxics
and leukemia in New Jersey, 2) mercury emissions and fish advisories in
the United States, and 3) urban sprawl and obesity in New Jersey. These
pilots illustrate the feasibility of creating environmental hazard and
health outcome indicators, examining temporal and geographic trends in
the indicators, and identifying temporal and geographic relationships.
The results highlight how existing environmental health data can be used
to create meaningful indicator measures and facilitate hypothesis
generation. Visualizing indicators spatially, temporally, and in
relation to one another can provide critical assistance to state and
local public health agencies trying to create and prioritize interventions, and to researchers seeking to better understand
environment-related diseases.

1. Air Toxics and Leukemia in New Jersey

1a. Overview

Air toxics and leukemia indicators have been developed at the
county level for the purpose of tracking leukemia incidence rates,
emissions of three air toxics associated with leukemia--benzene; 1,3
butadiene; and ethylene oxide--and the relationships between them
(Hughes, Meek, Walker & Beauchamp, 2003; Kirman et al., 2004;
Snyder, 2000).

1b. Data

The leukemia indicator was incidence, with elevation defined as
incidence greater than the national average (12.3 per 100,000) (National
Cancer Institute [NCI], 2004). County leukemia incidence rates for
1986-1996 were obtained from the New Jersey State Cancer Registry, and
the national average leukemia incidence for 1997-2001 came from NCI
(NCI, 2004; New Jersey Department of Health and Senior Services
[NJDHSS], 1998).

The air toxics indicator was risk ratios (relative risks) summed
across the three chemicals; levels >1 were defined as high. Emissions
data for benzene; 1,3 butadiene; and ethylene oxide were obtained from
the 1990 National Air Toxics Assessment (NATA). We used data from 1990
to allow for the approximately 10-year latency period for leukemia
associated with chemical exposure (Kirman et al., 2004). Air toxics risk
ratios were calculated according to the Assessment System for Population
Exposure Nationwide (ASPEN) dispersion model (U.S. EPA, 1990).

1c. Analyses

Descriptive

Descriptive analyses were carried out to determine the average and
range of leukemia incidence rates and air toxic risk ratios in New
Jersey counties.

Geographic trend analysis using ArcGIS 8.0 identified counties with
both high leukemia incidence and high emissions.

1d. Results

Descriptive

The average leukemia incidence rate across New Jersey counties
between 1997 and 2001 was 12.3 cases per 100,000, compared with the
national average of 11.2 per 100,000 (NCI, 2004; NJDHSS, 1998). Average
risk ratios across New Jersey counties for benzene; 1,3 butadiene; and
ethylene oxide were 25.1, 43.7, and 1, respectively. Risk ratios for
benzene and 1,3 butadiene suggested that the magnitude of the risk of
developing cancer or noncancer health outcomes was much higher for
individuals living in New Jersey than for individuals not living in New
Jersey (25.1 times and 43.7 times respectively).

[FIGURE 1 OMITTED]

Trend

Figure 1 shows the leukemia time trend analysis for white males and
females between 1986 and 1996. Rates decreased gradually for both
genders, a result consistent with the national trend (NCI, 2004). Maps
did not reveal geographic trends in air toxics or leukemia.

Linkage

Bar charts depicting relationships between high air toxic emissions
and high leukemia incidence rates were created. Individual charts for
each of the three air toxics did not show apparent associations with
leukemia risk. A combined chart is presented in Figure 2. Counties are
arranged in ascending order along the x-axis according to cumulative
emissions of benzene; 1,3 butadiene; and ethylene oxide. The y-axis
displays leukemia incidence rates per 100,000 for each county. No
apparent relationship was observed.

1e. Discussion

The average leukemia incidence rate across New Jersey counties was
12.3 per 100,000, compared with the national rate of 11.2 per 100,000,
suggesting that people in New Jersey are at a greater risk of leukemia
than elsewhere in the nation. Furthermore, all counties except Atlantic,
Cumberland, Union, and Hudson had rates above the national average. Many
New Jersey counties had air toxics risk ratios >1 for both benzene
and 1,3 butadiene, but not for ethylene oxide. Average risk ratios for
benzene and 1,3 butadiene were 25.1 and 43.7, respectively, suggesting
the potential for adverse health effects. Counties with high air toxic
risk ratios did not, however, appear to have higher leukemia incidence
rates.

There are some important limitations to take into account with
respect to the analyses. Incomplete reporting of leukemia incidence in
certain counties may have prevented a relationship between air toxics
and leukemia from being observed. In addition, the use of the 1990 NATA
data, which provided only modeled estimates of exposure at the county
level as opposed to real-time data at a smaller geographic scale, may
have had an impact on the findings of our study. The use of a smaller
geographic scale and real-time air quality monitoring would allow for
greater sensitivity in future studies to detect a relationship between
leukemia incidence and air toxics exposure. The analysis also did not
take into account potentially important variables, including time of
diagnosis and exposure to leukemia-causing agents such as tobacco smoke.
The use of statistical methods to control for confounders, especially
tobacco use, would strengthen future research.

[FIGURE 2 OMITTED]

The indicators in this study provided important geographic
information about the distribution of leukemia and air toxics. On the
basis of such information, practitioners can develop hypotheses about
risk factors and can target intervention investments such as screening,
awareness building, and communication with regulators about pollution
sources. From a research perspective, looking geographically at the two
indicators provides the opportunity to hypothesize, for example, about
the different leukemia-protective factors operating in rural and urban
counties.

2. Mercury Emissions and Fish Advisories in the United States

2a. Overview

Mercury air emissions and fish advisory indicators were developed
to examine the geographic and temporal distribution, trends, and
relationships in U.S. states with high mercury emissions and those with
high fish advisory levels. Adverse health effects from mercury exposure
include neurotoxic effects in the developing fetus and cardiovascular
effects in men (U.S. EPA, 1997; Guallar et al., 2002). Linking mercury
emission sources with deposition (measured by fish advisories) improves
understanding of the link between mercury air emissions and health
outcomes.

2b. Data

The mercury indicator was state air emissions in pounds. States
were considered to have elevated mercury emissions if the measure
exceeded the national average (796 lbs in 2002, the selected year of
interest). Mercury emissions in pounds for air, surface water, land, and
underground releases were obtained from U.S. EPA's Toxics Release
Inventory (TRI) (U.S. EPA, 2004).

The fish advisory indicator was the percentage of lake acres and
river miles under advisory for each state. Levels were considered
elevated if the range exceeded national averages (30.5 percent of lake
acres and 18.0 percent of river miles in 2002). Fish advisory data for
mercury were obtained for all 50 states, for the years 1993 to 2002,
from U.S. EPA's NLFWA database (U.S. EPA, 2004).

2c. Analyses

Descriptive

Bar charts examined total air emissions, surface water discharges,
land releases, and on-site disposal for all 50 states. The percentage of
lake acres and river miles under mercury fish advisory also was examined
for all 50 states in 2002.

Trends and Linkage: Temporal

Line graphs analyzed trends in national mercury air emissions from
1988 to 2002 and percentage of lake acres and river miles under mercury
fish advisory from 1993 to 2002.

Trends and Linkage: Geographic

The geographic relationship between distribution of mercury air
emissions and fish advisory locations by mapping, using ArcGIS 8.0.

2d. Results

Descriptive

In 2002, the average state mercury emission was 796 lbs. The
highest-emitting states were Ohio, Alabama, and Utah. In 2002,30.5
percent of lake acres and 18.0 percent of river miles nationwide were
under mercury fish advisory.

Trend

In 1997 and in 1999, U.S. EPA changed the TRI reporting
requirements, causing jumps in the data between 1997-1998 and 1999-2000.
As shown in Figure 3, however, mercury air emissions decreased over each
time period for which reporting requirements were constant. It is
surmised that overall, mercury air emissions have decreased from 1988 to
2002. By contrast, the percentage of lake acres and river miles covered
by fish advisories has increased over time from 1993 to 2002.

Linkage

Mercury air emissions tend to be released from states in the South
to the Mid-Atlantic, particularly Texas and Ohio. As U.S. EPA models of
mercury transport and deposition predicted, mercury fish advisories were
concentrated in northern states (Great Lake states and Ohio River valley), northeastern states, and Florida (Figure 4). Ohio and North
Carolina had both high mercury emissions and high fish advisories.

2e. Discussion

The decline in mercury air emissions may be explained by factors
including federal bans on mercury additives in paints and pesticides,
mercury reduction in batteries, decreased coal use, and state
regulations (U.S. EPA, 1997). Even though air emissions are declining,
water deposition may still occur at high rates because past mercury
emissions continue to be cycled between air, land, and water. Emissions
from other countries may also be transported globally and deposited in
U.S. lakes and rivers. In addition, increased water testing and rising
awareness of mercury health effects may have resulted in an increase in
state-issued fish advisories (U.S. EPA, 1997).

[FIGURE 3 OMITTED]

A number of limitations must be taken into account in the analyses.
Lake and river advisories are issued in states predicted by U.S. EPA to
have high mercury deposition rates; however, in the absence of modeled
data, it is not known how the geographic locations of fish advisories
are related to the geographic distribution of mercury deposition. The
relationship may be confounded by global mercury transport patterns and
natural emissions sources. In addition, states are responsible for
developing their own advisory programs and determining when to issue
advisories. There is thus considerable variability between states in the
extent of monitoring, sampling frequency, and mercury threshold for
issuing advisories, making it difficult to draw conclusions about
national trends (U.S. EPA, 2004).

The databases were also limited. TRI reports did not
comprehensively describe mercury emissions, and data breaks hampered
trend assessment. The data were also not as complete in earlier years.
U.S. EPA's National Listing of Fish and Wildlife Advisories (NLFWA)
database relies on state testing and reporting; however, measurement
variability between states hinders comparisons. Further research with
more complete data is needed for a better understanding of the mercury
cycle, deposition patterns, and global transport.

Despite these limitations, the indicators developed in the project
described here help with geographic and temporal tracking of air
emissions and fish advisories. The information is useful for
understanding and evaluating the impact of regulations and other factors
on emissions generation and for evaluating the varying policy responses
to emissions. It can help states with the development of interventions
beyond fishing advisories. The fact that the geographic distributions of
mercury emissions and fish advisories in this analysis follow U.S. EPA
model predictions strengthens the joint use of these indicators. Their
combination provides one way of illustrating how mercury emissions in
one area affect health and lifestyle elsewhere.

3. Urban Sprawl and Obesity in New Jersey

3a. Overview

Indicators of obesity and urban sprawl were created because obesity
rates have reached epidemic proportions throughout the United States and
are related to a lack of physical activity in the population (U.S.
Department of Health and Human Services [U.S. DHHS], 2001. Studies have
found a relationship between urban sprawl, obesity, and participation in
physical activity (Ewing, Schmid, Killingsworth, Zlot, & Raudenbush,
2003).

[FIGURE 4 OMITTED]

3b. Data

Because one indication of urban sprawl is increased land
development in low-density areas, urban sprawl was defined as the
percentage of housing unit development in a county between 1990 and 2000
that occurred in low-density municipalities. Low-density municipalities
were defined as being below the state median housing unit density.
County data on housing unit density for the state of New Jersey were
obtained from the 1990 and 2000 U.S. censuses (U.S. Census Bureau, 2002;
United States Census Bureau, 2002a, 2000b).

As an obesity indicator, the proportion of the population expected
to be overweight or obese was estimated by county on the basis of the
Behavioral Risk Factor Surveillance System (BRFSS) sample after the age,
race, gender, and income of participants in the BRFSS were controlled
for by logistic regression. No more refined sources for population level
estimates of obesity exist. Data on body mass index (BMI) were obtained
from the 1996-2000 BRFSS (CDC, 1996-2000). These years were used because
for a number of counties in New Jersey, data on BMI were not available
for earlier time periods.

[FIGURE 5 OMITTED]

3c. Analyses

Descriptive

The range of urban sprawl and the increase in obesity in New Jersey
counties was analyzed.

Trend and Linkage: Temporal

To perform temporal analysis of the obesity measure, we plotted
county values in relation to their year. The urban sprawl indicator did
not allow for temporal trend analysis, because although 10 years were
covered, the indicator was a single value representing the change in
housing unit density over those years.

Trend and Linkage: Geographic

Urban sprawl and obesity indicators were mapped by county with
ArcGIS 8.0. To examine the relationship between urban sprawl and change
in the obesity indicator, we compared the change in the proportion of
overweight and obese county residents between 1996 and 2000 and the
amount of urban sprawl in each county between 1990 and 2000.

3d. Results

Descriptive

Counties in New Jersey had levels of urban sprawl ranging from 0
percent to 100 percent between 1990 and 2000. In 2000, the proportion of
residents expected to be overweight or obese in New Jersey counties
ranged from 53 percent to 76 percent.

Trend

The geographic analysis of the urban sprawl indicator, shown in
Figure 5, revealed that Morris and Cumberland counties experienced the
greatest amount of urban sprawl between 1990 and 2000, while Hudson and
Union had the least amount. Counties in northeastern New Jersey and
along the coast had less sprawl overall between 1990 and 2000 than other
New Jersey counties.

[FIGURE 6 OMITTED]

Geographic analysis of the obesity indicator found that the New
York and Philadelphia metro areas had slightly higher proportions of
overweight or obese residents than other parts of New Jersey. The change
in the proportion of overweight and obese residents in New Jersey
counties between 1996 and 2000 was not found to exhibit a spatial
pattern. Temporal analysis revealed that all counties for which data
were available, except Mercer County, had increased obesity and
overweight between 1996 and 2000.

Linkage

Figure 6 shows a bar graph depicting the level of sprawl calculated
for each county from 1990 to 2000 and the change in the proportion of
the population expected to be overweight or obese between 1996 and 2000.
There was not a consistent association between these indicators. A
geographic analysis of this association was also carried out, and a
consistent pattern was not observed.

3e. Discussion

The urban sprawl indicator identified counties with rising
consumption of undeveloped land. Such information can be used to target
areas for land preservation efforts. The overweight-and-obesity
indicator identified the counties with the greatest increases in
overweight and obese residents between 1996 and 2000--knowledge that can
be used to target interventions to curtail overweight and obesity.

Further analyses using the urban-sprawl indicator developed in this
paper should be carried out in other states to see if any connections
with overweight and obesity can be established. In addition, the
association of this indicator with physical activity and automobile use
should be examined. Such information would help in designing and
tracking intervention programs and policies to support the design of
communities that foster physical activity participation amongst
residents.

As with the other two pilot indicator projects, the conclusions
drawn from this analysis were limited by a number of factors. By 1990,
many counties in New Jersey had already experienced a great deal of
urban sprawl, making exposure to sprawled development patterns high
throughout the state. In addition, it is likely that a lag period
between sprawled development and effects on the population's health
exists. Thus, in order to see the effects of urban sprawl between 1990
and 2000 on the population's health, it may be necessary to examine
obesity and overweight changes over the decade after sprawl occurred.
Further research should be carried out using statistical analyses that
make it possible to account for a lag between urban sprawl and obesity.
In addition, examination of the relationship between urban sprawl and
obesity on a smaller geographic scale may allow for a relationship to be
observed.

Conclusion

Together, the pilot projects described in the paper demonstrate the
utility and feasibility of using indicators for environmental public
health tracking efforts. They show how indicators combining
environmental monitoring and public health tracking data can be valuable
tools for examining temporal and geographic trends that describe
potential environmental relationships of concern. Such information is
essential for quantification of environmental hazards, exposures, and
health outcomes and identifying priority areas for intervention. These
projects demonstrate that indicator linkage projects can successfully be
carried out with publicly available data sources and can be used to
establish trends, generate hypotheses, and identify future research
needs with respect to environmental exposures and public health
outcomes. That information can then be used by practitioners for the
development of interventions and research programs.

Several lessons can be drawn from the development of the indicators
presented in this paper. First, indicator development is restricted by
the availability, reliability, and consistency of data. Second,
multidisciplinary expertise and collaboration are needed to design and
track indicators that will be useful for policy. Finally, because
indicator projects may not be controlled studies, their results are
often difficult to interpret. Great care must be taken in the
communication of findings about environmental exposure and disease
relationships to the public. Words should be carefully chosen, caveats
should be highlighted and repeated, and clear legends should be placed
on every graphic. For linkage indicators examining both hazard/exposure
and outcomes, it must be emphasized that conjunction or lack thereof
provides only exploratory and potentially suggestive data about
distributions and trends. Controlled analyses are generally needed to
draw firmer conclusions.

Indicators are the foundation of environmental public health
tracking. Increased use and development of indicators is necessary for
establishment of a nationwide environmental public health tracking
network capable of tracking and linking environmental exposures and
health outcomes. As the nationwide environmental public health tracking
network develops, researchers must ensure that data needed for the
development of relevant environmental and health indicators are
collected in a usable format in the smallest geographic aggregations
feasible. Without such data, indicators cannot be developed for the
study of connections between health and the environment. The lessons
learned from the indicator projects described in this paper demonstrate
the feasibility and utility of using national and county level
indicators to study relationships between environmental hazards,
exposures, and health outcomes.

Acknowledgement: Our research was supported by the Johns Hopkins
Center for Excellence in Environmental Public Health Tracking through a
grant from the Centers for Disease Control and Prevention.

Centers for Disease Control and Prevention. (1996-2000). Behavioral
Risk Factor Surveillance System Survey data. Atlanta, Georgia: U.S.
Department of Health and Human Services, Centers for Disease Control and
Prevention.

U.S. Department of Health and Human Services. (2001). The Surgeon
General's call to action to prevent and decrease overweight and
obesity. Rockville, MD: U.S. Department of Health and Human Services,
Public Health Service Office of the Surgeon General.